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LIPIcs, Volume 156
1st Symposium on Foundations of Responsible Computing (FORC 2020)
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Leibniz International Proceedings in Informatics (LIPIcs)
Conference: Symposium on Foundations of Responsible Computing (FORC)
Publication Details
- published at: 2020-05-18
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-95977-142-9
- DBLP: db/conf/forc/forc2020
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Document
Complete Volume
LIPIcs, Volume 156, FORC 2020, Complete Volume
Abstract
LIPIcs, Volume 156, FORC 2020, Complete Volume
Cite as
1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 1-190, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@Proceedings{roth:LIPIcs.FORC.2020,
title = {{LIPIcs, Volume 156, FORC 2020, Complete Volume}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {1--190},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020},
URN = {urn:nbn:de:0030-drops-120157},
doi = {10.4230/LIPIcs.FORC.2020},
annote = {Keywords: LIPIcs, Volume 156, FORC 2020, Complete Volume}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization
Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as
1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 0:i-0:viii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{roth:LIPIcs.FORC.2020.0,
author = {Roth, Aaron},
title = {{Front Matter, Table of Contents, Preface, Conference Organization}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {0:i--0:viii},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.0},
URN = {urn:nbn:de:0030-drops-120168},
doi = {10.4230/LIPIcs.FORC.2020.0},
annote = {Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Efficient Candidate Screening Under Multiple Tests and Implications for Fairness
Abstract
When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy signals in order to estimate the worker’s skill. Traditional economics papers address screening models where employers access worker skill via a single noisy signal. In this paper, we extend this theoretical analysis to a multi-test setting, considering both Bernoulli and Gaussian models. We analyze the optimal employer policy both when the employer sets a fixed number of tests per candidate and when the employer can set a dynamic policy, assigning further tests adaptively based on results from the previous tests. To start, we characterize the optimal policy when employees constitute a single group, demonstrating some interesting trade-offs. Subsequently, we address the multi-group setting, demonstrating that when the noise levels vary across groups, a fundamental impossibility emerges whereby we cannot administer the same number of tests, subject candidates to the same decision rule, and yet realize the same outcomes in both groups. We show that by subjecting members of noisier groups to more tests, we can equalize the confusion matrix entries across groups, seemingly eliminating any disparate impact concerning outcomes.
Cite as
Lee Cohen, Zachary C. Lipton, and Yishay Mansour. Efficient Candidate Screening Under Multiple Tests and Implications for Fairness. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{cohen_et_al:LIPIcs.FORC.2020.1,
author = {Cohen, Lee and Lipton, Zachary C. and Mansour, Yishay},
title = {{Efficient Candidate Screening Under Multiple Tests and Implications for Fairness}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {1:1--1:20},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.1},
URN = {urn:nbn:de:0030-drops-120179},
doi = {10.4230/LIPIcs.FORC.2020.1},
annote = {Keywords: algorithmic fairness, random walk, inference}
}
Document
Metric Learning for Individual Fairness
Abstract
There has been much discussion concerning how "fairness" should be measured or enforced in classification. Individual Fairness [Dwork et al., 2012], which requires that similar individuals be treated similarly, is a highly appealing definition as it gives strong treatment guarantees for individuals. Unfortunately, the need for a task-specific similarity metric has prevented its use in practice. In this work, we propose a solution to the problem of approximating a metric for Individual Fairness based on human judgments. Our model assumes access to a human fairness arbiter who is free of explicit biases and possesses sufficient domain knowledge to evaluate similarity. Our contributions include definitions for metric approximation relevant for Individual Fairness, constructions for approximations from a limited number of realistic queries to the arbiter on a sample of individuals, and learning procedures to construct hypotheses for metric approximations which generalize to unseen samples under certain assumptions of learnability of distance threshold functions.
Cite as
Christina Ilvento. Metric Learning for Individual Fairness. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 2:1-2:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{ilvento:LIPIcs.FORC.2020.2,
author = {Ilvento, Christina},
title = {{Metric Learning for Individual Fairness}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {2:1--2:11},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.2},
URN = {urn:nbn:de:0030-drops-120183},
doi = {10.4230/LIPIcs.FORC.2020.2},
annote = {Keywords: metric learning, individual fairness, fair machine learning}
}
Document
Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?
Abstract
Multiple fairness constraints have been proposed in the literature, motivated by a range of concerns about how demographic groups might be treated unfairly by machine learning classifiers. In this work we consider a different motivation; learning from biased training data. We posit several ways in which training data may be biased, including having a more noisy or negatively biased labeling process on members of a disadvantaged group, or a decreased prevalence of positive or negative examples from the disadvantaged group, or both. Given such biased training data, Empirical Risk Minimization (ERM) may produce a classifier that not only is biased but also has suboptimal accuracy on the true data distribution. We examine the ability of fairness-constrained ERM to correct this problem. In particular, we find that the Equal Opportunity fairness constraint [Hardt et al., 2016] combined with ERM will provably recover the Bayes optimal classifier under a range of bias models. We also consider other recovery methods including re-weighting the training data, Equalized Odds, and Demographic Parity, and Calibration. These theoretical results provide additional motivation for considering fairness interventions even if an actor cares primarily about accuracy.
Cite as
Avrim Blum and Kevin Stangl. Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{blum_et_al:LIPIcs.FORC.2020.3,
author = {Blum, Avrim and Stangl, Kevin},
title = {{Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {3:1--3:20},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.3},
URN = {urn:nbn:de:0030-drops-120192},
doi = {10.4230/LIPIcs.FORC.2020.3},
annote = {Keywords: fairness in machine learning, equal opportunity, bias, machine learning}
}
Document
Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users
Abstract
We present a new concern when collecting data from individuals that arises from the attempt to mitigate privacy leakage in multiple reporting: tracking of users participating in the data collection via the mechanisms added to provide privacy. We present several definitions for untrackable mechanisms, inspired by the differential privacy framework.
Specifically, we define the trackable parameter as the log of the maximum ratio between the probability that a set of reports originated from a single user and the probability that the same set of reports originated from two users (with the same private value). We explore the implications of this new definition. We show how differentially private and untrackable mechanisms can be combined to achieve a bound for the problem of detecting when a certain user changed their private value.
Examining Google’s deployed solution for everlasting privacy, we show that RAPPOR (Erlingsson et al. ACM CCS, 2014) is trackable in our framework for the parameters presented in their paper.
We analyze a variant of randomized response for collecting statistics of single bits, Bitwise Everlasting Privacy, that achieves good accuracy and everlasting privacy, while only being reasonably untrackable, specifically grows linearly in the number of reports. For collecting statistics about data from larger domains (for histograms and heavy hitters) we present a mechanism that prevents tracking for a limited number of responses.
We also present the concept of Mechanism Chaining, using the output of one mechanism as the input of another, in the scope of Differential Privacy, and show that the chaining of an ε₁-LDP mechanism with an ε₂-LDP mechanism is ln (e^{ε₁+ε₂} + 1)/(e^ε₁ + e^ε₂)-LDP and that this bound is tight.
Cite as
Moni Naor and Neil Vexler. Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 4:1-4:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{naor_et_al:LIPIcs.FORC.2020.4,
author = {Naor, Moni and Vexler, Neil},
title = {{Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {4:1--4:20},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.4},
URN = {urn:nbn:de:0030-drops-120205},
doi = {10.4230/LIPIcs.FORC.2020.4},
annote = {Keywords: Differential Privacy, Surveillance}
}
Document
Service in Your Neighborhood: Fairness in Center Location
Abstract
When selecting locations for a set of centers, standard clustering algorithms may place unfair burden on some individuals and neighborhoods. We formulate a fairness concept that takes local population densities into account. In particular, given k centers to locate and a population of size n, we define the "neighborhood radius" of an individual i as the minimum radius of a ball centered at i that contains at least n/k individuals. Our objective is to ensure that each individual has a center that is within at most a small constant factor of her neighborhood radius.
We present several theoretical results: We show that optimizing this factor is NP-hard; we give an approximation algorithm that guarantees a factor of at most 2 in all metric spaces; and we prove matching lower bounds in some metric spaces. We apply a variant of this algorithm to real-world address data, showing that it is quite different from standard clustering algorithms and outperforms them on our objective function and balances the load between centers more evenly.
Cite as
Christopher Jung, Sampath Kannan, and Neil Lutz. Service in Your Neighborhood: Fairness in Center Location. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{jung_et_al:LIPIcs.FORC.2020.5,
author = {Jung, Christopher and Kannan, Sampath and Lutz, Neil},
title = {{Service in Your Neighborhood: Fairness in Center Location}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {5:1--5:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.5},
URN = {urn:nbn:de:0030-drops-120215},
doi = {10.4230/LIPIcs.FORC.2020.5},
annote = {Keywords: Fairness, Clustering, Facility Location}
}
Document
Bias In, Bias Out? Evaluating the Folk Wisdom
Abstract
We evaluate the folk wisdom that algorithmic decision rules trained on data produced by biased human decision-makers necessarily reflect this bias. We consider a setting where training labels are only generated if a biased decision-maker takes a particular action, and so "biased" training data arise due to discriminatory selection into the training data. In our baseline model, the more biased the decision-maker is against a group, the more the algorithmic decision rule favors that group. We refer to this phenomenon as bias reversal. We then clarify the conditions that give rise to bias reversal. Whether a prediction algorithm reverses or inherits bias depends critically on how the decision-maker affects the training data as well as the label used in training. We illustrate our main theoretical results in a simulation study applied to the New York City Stop, Question and Frisk dataset.
Cite as
Ashesh Rambachan and Jonathan Roth. Bias In, Bias Out? Evaluating the Folk Wisdom. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 6:1-6:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{rambachan_et_al:LIPIcs.FORC.2020.6,
author = {Rambachan, Ashesh and Roth, Jonathan},
title = {{Bias In, Bias Out? Evaluating the Folk Wisdom}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {6:1--6:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.6},
URN = {urn:nbn:de:0030-drops-120225},
doi = {10.4230/LIPIcs.FORC.2020.6},
annote = {Keywords: fairness, selective labels, discrimination, training data}
}
Document
Individual Fairness in Pipelines
Abstract
It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline composition. Pipelines differ from ordinary sequential or repeated composition in that individuals may drop out at any stage, and classification in subsequent stages may depend on the remaining "cohort" of individuals. As an example, a company might hire a team for a new project and at a later point promote the highest performer on the team. Unlike other repeated classification settings, where the degree of unfairness degrades gracefully over multiple fair steps, the degree of unfairness in pipelines can be arbitrary, even in a pipeline with just two stages.
Guided by a panoply of real-world examples, we provide a rigorous framework for evaluating different types of fairness guarantees for pipelines. We show that naïve auditing is unable to uncover systematic unfairness and that, in order to ensure fairness, some form of dependence must exist between the design of algorithms at different stages in the pipeline. Finally, we provide constructions that permit flexibility at later stages, meaning that there is no need to lock in the entire pipeline at the time that the early stage is constructed.
Cite as
Cynthia Dwork, Christina Ilvento, and Meena Jagadeesan. Individual Fairness in Pipelines. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 7:1-7:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{dwork_et_al:LIPIcs.FORC.2020.7,
author = {Dwork, Cynthia and Ilvento, Christina and Jagadeesan, Meena},
title = {{Individual Fairness in Pipelines}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {7:1--7:22},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.7},
URN = {urn:nbn:de:0030-drops-120235},
doi = {10.4230/LIPIcs.FORC.2020.7},
annote = {Keywords: algorithmic fairness, fairness under composition, pipelines}
}
Document
Abstracting Fairness: Oracles, Metrics, and Interpretability
Abstract
It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We examine what can be learned from a fairness oracle equipped with an underlying understanding of "true" fairness. The oracle takes as input a (context, classifier) pair satisfying an arbitrary fairness definition, and accepts or rejects the pair according to whether the classifier satisfies the underlying fairness truth. Our principal conceptual result is an extraction procedure that learns the underlying truth; moreover, the procedure can learn an approximation to this truth given access to a weak form of the oracle. Since every "truly fair" classifier induces a coarse metric, in which those receiving the same decision are at distance zero from one another and those receiving different decisions are at distance one, this extraction process provides the basis for ensuring a rough form of metric fairness, also known as individual fairness.
Our principal technical result is a higher fidelity extractor under a mild technical constraint on the weak oracle’s conception of fairness. Our framework permits the scenario in which many classifiers, with differing outcomes, may all be considered fair.
Our results have implications for interpretablity - a highly desired but poorly defined property of classification systems that endeavors to permit a human arbiter to reject classifiers deemed to be "unfair" or illegitimately derived.
Cite as
Cynthia Dwork, Christina Ilvento, Guy N. Rothblum, and Pragya Sur. Abstracting Fairness: Oracles, Metrics, and Interpretability. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{dwork_et_al:LIPIcs.FORC.2020.8,
author = {Dwork, Cynthia and Ilvento, Christina and Rothblum, Guy N. and Sur, Pragya},
title = {{Abstracting Fairness: Oracles, Metrics, and Interpretability}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {8:1--8:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.8},
URN = {urn:nbn:de:0030-drops-120247},
doi = {10.4230/LIPIcs.FORC.2020.8},
annote = {Keywords: Algorithmic fairness, fairness definitions, causality-based fairness, interpretability, individual fairness, metric fairness}
}
Document
The Role of Randomness and Noise in Strategic Classification
Abstract
We investigate the problem of designing optimal classifiers in the "strategic classification" setting, where the classification is part of a game in which players can modify their features to attain a favorable classification outcome (while incurring some cost). Previously, the problem has been considered from a learning-theoretic perspective and from the algorithmic fairness perspective.
Our main contributions include
- Showing that if the objective is to maximize the efficiency of the classification process (defined as the accuracy of the outcome minus the sunk cost of the qualified players manipulating their features to gain a better outcome), then using randomized classifiers (that is, ones where the probability of a given feature vector to be accepted by the classifier is strictly between 0 and 1) is necessary.
- Showing that in many natural cases, the imposed optimal solution (in terms of efficiency) has the structure where players never change their feature vectors (and the randomized classifier is structured in a way, such that the gain in the probability of being classified as a "1" does not justify the expense of changing one’s features).
- Observing that the randomized classification is not a stable best-response from the classifier’s viewpoint, and that the classifier doesn’t benefit from randomized classifiers without creating instability in the system.
- Showing that in some cases, a noisier signal leads to better equilibria outcomes - improving both accuracy and fairness when more than one subpopulation with different feature adjustment costs are involved. This is particularly interesting from a policy perspective, since it is hard to force institutions to stick to a particular randomized classification strategy (especially in a context of a market with multiple classifiers), but it is possible to alter the information environment to make the feature signals inherently noisier.
Cite as
Mark Braverman and Sumegha Garg. The Role of Randomness and Noise in Strategic Classification. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 9:1-9:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{braverman_et_al:LIPIcs.FORC.2020.9,
author = {Braverman, Mark and Garg, Sumegha},
title = {{The Role of Randomness and Noise in Strategic Classification}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {9:1--9:20},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.9},
URN = {urn:nbn:de:0030-drops-120255},
doi = {10.4230/LIPIcs.FORC.2020.9},
annote = {Keywords: Strategic classification, noisy features, randomized classification, fairness}
}
Document
Bounded-Leakage Differential Privacy
Abstract
We introduce and study a relaxation of differential privacy [Dwork et al., 2006] that accounts for mechanisms that leak some additional, bounded information about the database. We apply this notion to reason about two distinct settings where the notion of differential privacy is of limited use. First, we consider cases, such as in the 2020 US Census [Abowd, 2018], in which some information about the database is released exactly or with small noise. Second, we consider the accumulation of privacy harms for an individual across studies that may not even include the data of this individual. The tools that we develop for bounded-leakage differential privacy allow us reason about privacy loss in these settings, and to show that individuals preserve some meaningful protections.
Cite as
Katrina Ligett, Charlotte Peale, and Omer Reingold. Bounded-Leakage Differential Privacy. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{ligett_et_al:LIPIcs.FORC.2020.10,
author = {Ligett, Katrina and Peale, Charlotte and Reingold, Omer},
title = {{Bounded-Leakage Differential Privacy}},
booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)},
pages = {10:1--10:20},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-142-9},
ISSN = {1868-8969},
year = {2020},
volume = {156},
editor = {Roth, Aaron},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.10},
URN = {urn:nbn:de:0030-drops-120265},
doi = {10.4230/LIPIcs.FORC.2020.10},
annote = {Keywords: differential privacy, applications, privacy, leakage, auxiliary information}
}